US8547550B2 - Biological and chemical collection and detection - Google Patents

Biological and chemical collection and detection Download PDF

Info

Publication number
US8547550B2
US8547550B2 US13/260,973 US201013260973A US8547550B2 US 8547550 B2 US8547550 B2 US 8547550B2 US 201013260973 A US201013260973 A US 201013260973A US 8547550 B2 US8547550 B2 US 8547550B2
Authority
US
United States
Prior art keywords
sample
subsystem
collected
organisms
identification
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active, expires
Application number
US13/260,973
Other languages
English (en)
Other versions
US20120040330A1 (en
Inventor
Jeffrey P. Carpenter
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Battelle Memorial Institute Inc
Original Assignee
Battelle Memorial Institute Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Battelle Memorial Institute Inc filed Critical Battelle Memorial Institute Inc
Priority to US13/260,973 priority Critical patent/US8547550B2/en
Publication of US20120040330A1 publication Critical patent/US20120040330A1/en
Application granted granted Critical
Publication of US8547550B2 publication Critical patent/US8547550B2/en
Active legal-status Critical Current
Adjusted expiration legal-status Critical

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor

Definitions

  • the present invention relates in general to Raman identification of particles and in particular, to fluorescence-cued Raman identification of viable organisms.
  • Standard microbiological methods developed to determine whether bacterial cells are dead or alive generally rely upon the ability of the tested bacterial cells to grow and produce colonies as a measure of the viability of the bacterial cells under test.
  • industries such as the pharmaceutical industry are concerned about the identification of viable particles including organisms that are viable, but non-culturable.
  • certain bacteria may go into a viable but non-culturable state wherein the bacterial organisms have metabolic processes but are not undergoing cell division. In this state, bacteria are still viable, but may not be shown as colony forming units under nonselective growth conditions using common techniques such as conventional plate counts.
  • Such viable but non-culturable bacteria may comprise a concern, for example, in public health risk assessments because certain pathogenic bacteria may be able to transition between a viable but non-culturable state and an infectious state.
  • a method of identifying organisms of interest comprises collecting a sample, such as an environmental sample or product sample.
  • the method further comprises treating the collected sample with a reagent that identifies whether there are any organisms of interest within the collected sample.
  • a potential viable organism of interest such as a viable cell
  • the reagent may include for example, a viability stain that fluoresces when activated by a cell's processes, a reporter dye, an assay that renders any living organism detectable, etc.
  • the method further comprises collecting image data of the sample using a microscope, e.g., by transferring the sample to an automated microscope stage and using the microscope stage to collect images of the sample, and locating any potential viable organisms of interest within the collected sample by utilizing image processing to automatically locate whether there are occurrences of the marker within at least one image of the collected sample.
  • the method comprises identifying at least one target location within the sample for spectral interrogation of a potential viable organism, each target location selected based upon the location of a corresponding instance of the marker within the sample that is derived from the collected image data.
  • the method also further comprises obtaining spectral information of each identified target location within the sample, analyzing any obtained spectral information to automatically establish a preliminary identification of at least one corresponding located potential viable organism, each preliminary identification made to at least a small ambiguity of the identification and providing information related to the result of the preliminary identification for a final confirmation by a user.
  • a system for identifying organisms comprises a plurality of subsystems that cooperate to implement a fluorescence-cued Raman identification system.
  • the subsystems include a collection subsystem that collects a sample onto a filter and a reagent treatment subsystem that receives the sample from the collection subsystem and treats the sample collected by the collection subsystem using a reagent that identifies whether there are any organisms of interest within the sample, where a potential viable organism of interest is distinguishable based at least in part, by a marker produced as a result of a reaction between the reagent and the potential viable organism of interest.
  • the subsystems further comprise a fluorescence imaging subsystem that receives the treated sample from the reagent treatment subsystem and that automatically takes at least one image of the collected sample.
  • the subsystems further include a Raman spectroscopy subsystem that receives the sample from the fluorescence subsystem and that measures the spectrum of potential viable organisms that are located based upon at least one image of the collected sample and a visible imaging microscope subsystem that provides a visual image of any organisms analyzed by the Raman spectroscopy subsystem.
  • the system further comprises a processor that performs image processing to locate potential viable organisms within the sample, which are targeted by the Raman spectroscopy subsystem.
  • the processor further analyzes the spectrum recorded by the Raman spectroscopy subsystem to make a preliminary identification of the targeted organisms for confirmation by a user using the visible imaging microscope subsystem.
  • FIG. 1 is a schematic illustration of a fluorescence-cued Raman identification system for the identification of viable organisms according to various aspects of the present invention
  • FIG. 2 is a flow diagram of the fluorescence-cued Raman identification system of FIG. 1 , according to various aspects of the present invention.
  • FIG. 3 is a flow chart illustrating a method of implementing fluorescence-cued identification according to various aspects of the present invention.
  • a fluorescence-cued Raman identification system that utilizes fluorescence assays to detect one or more organisms of interest.
  • the fluorescence assay may be a general assay for all/multiple types of viable organisms or alternatively, a specialized assay for targeting a specific organism or organisms.
  • the above fluorescence assays may be utilized to target viable organisms, such as viable cells, in either spore or vegetative states within collected samples.
  • a selected chemical fluorescence assay may be used for targeting viable organisms collected in a sample.
  • the reaction of specific locations within the sample to the chemical fluorescence assay is used to select out and target regions of the collected sample that are of interest for further interrogation.
  • Spectral data is collected from the targeted sample regions that were selected from within the larger sample for identification, e.g., using a Raman spectrometer.
  • the spectral data collected from the targeted interrogation is evaluated to generate an unambiguous identification of the targeted organism(s).
  • the evaluation of the spectral data may result in an identification of the targeted organism(s) within a small ambiguity group.
  • the system may then present information related to the targeted interrogation to a human operator for verification and/or interpretation of the machine implemented operations. For instance, in an exemplary implementation, the system presents information related to any cells that were detected or otherwise classified as viable or likely viable within a small ambiguity group to an operator.
  • the information presented by the system to a human operator for their concurrence with the machine identification may take the form of a microscope or other visual image or images of the cells judged by the system to be viable or likely viable.
  • microbiologists may be able to conduct rapid, objective determinations as to whether or not a sample is likely to contain viable organisms.
  • the fluorescence-cued Raman identification system 10 includes, in general, a plurality of subsystems that cooperate to implement the functionality of fluorescence-cued Raman identification as described more fully herein.
  • the illustrated system 10 includes six subsystems including a collection subsystem 12 , a reagent treatment subsystem 14 , a fluorescence imaging subsystem 16 , a Raman spectroscopy subsystem 18 , an automated stage subsystem 20 , and a visible imaging microscope subsystem 22 .
  • the various logical and functional features of two or more subsystems may be combined, consolidated or otherwise integrated into a common physical structure.
  • the fluorescence imaging subsystem 16 , the Raman spectroscopy subsystem 18 , the automated stage subsystem 20 , and the visible imaging microscope subsystem 22 are integrated into a Raman microscope system 24 .
  • Other logical and/or physical groupings of the various subsystems may also be implemented, depending for example, upon the particular implementation.
  • a processor 26 is optionally included, e.g., as a processing device that is integrated or otherwise in communication various subsystems of the system 10 .
  • the collection subsystem 12 collects a sample onto a filter.
  • the reagent treatment subsystem 14 receives the sample from the collection subsystem 12 and treats the sample collected by the collection subsystem 12 using a reagent that identifies whether there are any organisms of interest within the sample.
  • a potential viable organism of interest is distinguishable based at least in part, by a marker produced as a result of a reaction between the reagent and the potential viable organism of interest.
  • the fluorescence imaging subsystem 16 receives the treated sample from the reagent treatment subsystem 14 and automatically takes at least one image of the collected sample.
  • the Raman spectroscopy subsystem 18 receives the sample from the fluorescence subsystem 16 and measures the spectrum of potential viable organisms that are located based upon at least one image of the collected sample.
  • the visible imaging microscope subsystem 22 provides a visual image of any organisms analyzed by the Raman spectroscopy subsystem 18 .
  • the automation stage that automatically transports the filter between at least two subsystems. Further, the processor 26 performs image processing to locate potential viable organisms within the sample, which are targeted by the Raman spectroscopy subsystem 18 . The processor 26 further analyzes the spectrum recorded by the Raman spectroscopy subsystem 18 to make a preliminary identification of the targeted organisms for confirmation by a user using the visible imaging microscope subsystem 22 .
  • the processor 26 performs the necessary processing, e.g., to control one or more of the hardware components of the system 10 , to perform software processing, classification, analysis, image processing, data management, etc.
  • the processor 26 is also capable of direct or indirect communication with other devices, circuits or other processes, such as memory 28 and input output (I/O) 30 .
  • the processor 26 can communicate image data, spectral data, preliminary identification information etc., to external sources, as will be described in greater detail herein.
  • the memory 28 can be implemented as any combination of one or more computer readable storage medium(s) having computer readable program code embodied therewith.
  • the computer readable storage medium is implemented as a tangible medium to contain or store a program, data or other information for use by or in connection with an instruction execution system, apparatus, or device, e.g., the processor 26 for carrying out operations described more fully herein.
  • Each computer readable storage medium may utilize, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor or any suitable technology or combination of technologies.
  • the memory 28 may be implemented as a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, etc.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM compact disc read-only memory
  • optical storage device a magnetic storage device, etc.
  • the I/O 30 facilitates interaction with users and/or other electronic devices.
  • Exemplary I/O 30 may include a network interface or other adapter to facilitate communication with other electronic devices, e.g., across a network.
  • Exemplary I/O 30 further comprises devices and circuits for interacting with a user, such as a display screen, video adapter, keypad or keyboard, mouse or other pointing device, keypad/mouse adapter, etc.
  • the processor 26 executes computer program instructions, e.g., stored in the memory 28 , to perform image processing of image data collected by the fluorescence imaging subsystem 16 to locate viable organisms within a collected sample. Based upon the results of the image processing, the processor 26 defines targeted regions within the collected image data. The target region information defined by the processor 26 is utilized by the Raman spectroscopy subsystem 26 to collect Raman spectral data by performing targeted interrogation of the collected sample at the targeted region(s). The processor 26 then executes computer program code to analyze the spectral information recorded by the Raman spectroscopy subsystem 24 to make a preliminary identification of the targeted organisms, etc., as will be described in greater detail herein. In this regard, the processor 26 may interact with memory/storage 28 and/or I/O 30 to retrieve program code, data files, classifiers, control parameters or any other information or program code necessary to perform functions as set out in greater detail herein.
  • the collection subsystem 12 collects a sample onto a sample substrate.
  • the collection subsystem 12 utilizes a collection device 32 to collect a sample onto an appropriate sample substrate, e.g., a filter.
  • the collection device 32 may comprise, for example, an aerosol collector, solid surface small area impactor, electrostatic precipitation device, cyclone device or other collection technology.
  • the collection device 32 draws and accelerates a fluid stream, such as from the ambient air, through the collector.
  • the filter 34 is any suitable substrate or other arrangement for holding a collected sample.
  • the filter 34 can be implemented as a non-Raman active membrane filter 34 .
  • the sample substrate may comprise other forms of sample containing material.
  • the filter 34 is manually placed in the collection subsystem 12 .
  • the filter 34 is automatically placed in the collection subsystem 12 , e.g., via a translation feature of the automation subsystem 20 .
  • the automation subsystem 20 can utilize an automation mechanism to retrieve a filter 34 , e.g., from a substrate storage area or other location, and transport the filter 34 to the collection device 32 .
  • the automation stage 20 utilizes a rotational stage, linear stage or other arrangement of features to transport the filter 34 between one or more of the subsystems as described more fully herein.
  • the processor 26 can control the automation stage 20 , e.g., to establish the timing and flow of the filter 34 through the system 10 .
  • the system 10 is configured to sample the air for particulates that fall within a range of sizes that can be inhaled.
  • the biological and chemical detection system 10 may be used to discriminate nitrate and sulfate, ammonium, fungal spores and other particulates in collected samples.
  • the collection device 32 e.g., a small area collector, is designed to collect and deposit particulates generally within the size range of approximately 1 ⁇ m to 10 ⁇ m on the filter 34 .
  • the collection subsystem 12 can collect other types of environmental samples, such as water samples, product samples, process samples, etc., using appropriate sampling techniques. As such, the particular collection subsystem implementation may vary, depending for example, upon the type of sample to be collected.
  • the filter 34 carrying the collected sample is relocated to the reagent treatment subsystem 14 .
  • the relocation of the filter 34 may be carried out automatically via translation by the automation subsystem 20 e.g., using a linear translation stage, a rotational translation stage, etc.
  • the reagent treatment subsystem 14 may otherwise receive the sample from the collection subsystem 12 , e.g., via manual relocation. Regardless, the reagent treatment subsystem 14 treats the samples collected by the collection subsystem 12 using reagents that identify organisms collected onto the filter 34 .
  • the sample on the filter 34 is treated at the reagent treatment subsystem 14 , using a reagent and/or equipment, such as from a vendor such as AES Chemunex, Inc., of Princeton N.J., USA; MicroLink (Milliflex) of Cork Ireland or Celsis International, Cambridge United Kingdom.
  • a reagent and/or equipment such as from a vendor such as AES Chemunex, Inc., of Princeton N.J., USA; MicroLink (Milliflex) of Cork Ireland or Celsis International, Cambridge United Kingdom.
  • the treatments cause viable organisms collected on the filter 34 to be distinguishable.
  • a treatment such as a viability stain may cause one or more types of viable organism to emit a glow that is stable for a finite period of time, e.g., several minutes.
  • the treatments may also/alternatively utilize a reporter dye that provides a specific spectral response to a corresponding excitation. While many such assays detect any living organism, certain assays are available that can be targeted at a specific organism or organisms. In this regard, the selection of the specific treatment will depend upon the application and/or organism(s) of interest.
  • the filter 34 is identified as a “treated filter 34 A” to designate that the sample collected onto the filter 34 has been treated using a reagent.
  • the filter 34 A Upon treatment of the sample by the reagent treatment subsystem 14 , the filter 34 A is relocated to the fluorescence imaging subsystem 16 .
  • the relocation of the filter 34 A is carried out automatically via translation by the automation subsystem 20 e.g., using a linear translation stage, a rotational translation stage, etc., in a manner analogous to that described above.
  • the fluorescence imaging subsystem 16 may otherwise receive the treated filter 34 A, e.g., via manual relocation.
  • the fluorescence imaging subsystem 16 receives the treated sample on the filter 34 A from the reagent treatment subsystem 14 and automatically takes at least one image of the collected sample. For example, an automated microscope stage 36 within the fluorescence imaging subsystem 16 takes at least one focused image 38 of the sample collected on the treated filter 34 A. Image processing locates any viable organisms on the treated filter 34 by identifying at least one distinguishing feature that is rendered discoverable by the treatment. In an exemplary implementation, the processor 26 analyzes data corresponding to the one or more images collected by the automated microscope stage 36 of the fluorescence imaging subsystem 16 to identify cells/particles/viable organisms, etc., identified by the marker(s) left behind as a result of treatment at the reagent treatment subsystem 14 .
  • the processor 26 optionally further controls system level operation of the fluorescence imaging subsystem 16 , e.g., by instructing the automated microscope when to begin collecting image(s) and/or by instructing the automated microscope 36 as to how many images to collect.
  • the automated microscope can take at least one image under the control of the processor 26 .
  • the processor 26 also optionally controls operational set points, e.g., operating parameters of the automated microscope 36 , e.g., by setting or otherwise influencing the magnification, focus, exposure, location within the treated filter 34 A, etc.
  • the processor 26 further utilizes image processing to automatically locate whether there are occurrences of the marker within at least one image of the collected sample. In performing such image analysis, the processor 26 can perform data processing, such as image data filtering, processing, classification, statistical analysis, feature or pattern recognition, etc.
  • the filter 34 A Upon imaging of the sample by the fluorescence imaging system 16 , the filter 34 A is relocated to the Raman spectroscopy system 18 .
  • the relocation of the filter 34 A is carried out automatically via translation by the automation subsystem 20 e.g., using a linear translation stage or a rotational translation stage.
  • the Raman spectroscopy system 18 receives the treated filter 34 A, e.g., via manual relocation. Still further, there may not be a need to physically relocate the filter 34 , e.g., where both the Fluorescence imaging subsystem 16 and the Raman spectroscopy subsystem 18 can both interact with the filter 34 positioned at a common physical location, e.g., using optics, etc.
  • the Raman spectroscopy subsystem 18 measures the spectrum of any detected potential viable organisms that are located based upon an analysis of the focused image(s) of the sample collected by the fluorescence imaging subsystem 16 .
  • the processor 26 determines target locations based upon the identification of markers located within at least one image and feeds the necessary coordinates derived from image processing of the image data collected by the fluorescence imaging subsystem 16 to a Raman spectrometer 40 of the Raman spectroscopy subsystem 18 .
  • the Raman spectrometer thus receives target location information from the processor 26 so as to target specific potential viable organisms within the sample for targeted interrogation to measure the spectrum of viable organisms.
  • the filter 34 is now relabeled as targeted filter 34 B to designate that a sample on the filter 34 has been treated and targeted for interrogation.
  • each targeted cell, organism, feature, etc., within the sample that has been selected for targeted interrogation is moved under the Raman system where a spectrum of the target location is measured by the Raman spectrometer 40 .
  • the processor 26 analyzes the collected spectral information to make a preliminary assessment, e.g., to provide a preliminary identification of an organism positioned at the target location or to identify an organism within a small ambiguity.
  • the analysis of the processor 26 repeats for each targeted location that is analyzed by the Raman spectrometer 40 .
  • the processor 26 optionally further controls system level operation of the Raman spectroscopy subsystem 18 , e.g., by instructing the automated Raman spectrometer 40 when to begin collecting spectra, where to target the sample substrate and/or by instructing the Raman spectrometer 40 as to how many spectra to collect.
  • the processor 26 also optionally controls operational set points, e.g., operating parameters of the Raman spectrometer 40 , e.g., by setting or otherwise influencing the magnification, and/or other parameters that influence the collection of spectral information, etc.
  • the processor 26 may perform image data filtering, processing, classification, statistical analysis, feature or pattern recognition, etc., of the collected spectral information.
  • the targeted and treated filter 34 B is located for processing at the visible imaging microscope subsystem 22 .
  • the filter 34 is automatically relocated, e.g., via translation by the automation subsystem 20 e.g., using a linear translation stage or a rotational translation stage.
  • the visible imaging microscope subsystem 22 receives the filter 34 , e.g., via manual relocation.
  • there may not be a need to physically relocate the filter 34 e.g., where both the Raman spectroscopy subsystem 18 and the visible imaging microscope subsystem 22 can both interact with the filter 34 positioned at a common physical location, e.g., using optics, etc.
  • the processor 26 analyzes the spectrum recorded by the Raman spectroscopy subsystem to make a preliminary identification of the targeted organisms within at least a small ambiguity of the organism under evaluation.
  • the visible imaging microscope subsystem 22 provides a visual image of the targeted locations, e.g., particle(s), organism(s), etc., analyzed based upon the spectral data collected by the Raman spectroscopy subsystem 18 .
  • the visible imaging microscope subsystem 22 gives the operator a visual image of each targeted particle and allows the operator to make the final determination as to the nature of the particulate.
  • the processor 26 conveys the preliminary assessment to the user so that the user can compare the results of the preliminary assessment with actual subjective user interpretation of the sample collected onto the filter 34 .
  • the visual confirmation implemented by the user based upon the preliminary assessment need not be limited to a false positive confirmation of specifically selected target locations. Rather, the user is free to scan other portions of the sample collected onto the filter 34 , e.g., to provide a check against false negative results or to evaluate locations for organisms or organism varieties that may not be within the capability of the processor 26 to properly identify.
  • the various aspects of the present invention are not limited to detecting specific viable organisms, but rather, for the identification of pathogens, allergens, bacteria, viruses, fungi, biological agents, other viable microorganisms and/or chemical particulates, etc.
  • the processor 26 implements data analysis algorithms that analyze the interrogation results, e.g., the spectral data collected by the Raman spectrometer 40 , to determine whether biological or chemical particulates of interest are present in the sample area.
  • the processor 26 also executes one or more appropriate action events based upon the analysis of the interrogation results.
  • the processor 26 sounds an alarm or otherwise communicates an appropriate signal if biological or chemical particulates of interest are identified in the sample, e.g., within a targeted region of the filter 34 .
  • the processor 26 also optionally writes or otherwise stores logs, records or other indications to memory 28 , e.g., with regard to the interrogation results and other relevant or otherwise desired information of interest.
  • the method 100 comprises collecting a sample at 102 .
  • a sample For example, an environmental sample, water sample, product sample, process sample, etc., may be collected onto a filter material.
  • the sample is collected using an aerosol collector to sample the air and deposit the collected sample onto a non-Raman active membrane filter.
  • the method further comprises treating the collected sample at 104 .
  • the collected sample is treated with a reagent that identifies whether there are any organisms of interest within the sample, where a potential viable organism of interest is distinguishable based at least in part, by a marker produced as a result of a reaction between the reagent and the potential viable organism of interest.
  • the collected sample is treated with a viability stain to identify whether there are any organisms of interest collected within the sample, wherein an organism of interest is distinguishable based at least in part, because of the viability stain.
  • the collected sample is treated with a viability stain that fluoresces or otherwise causes viable organisms of interest to emit a glow that is stable for at least a finite period of time, when activated by a corresponding cell's processes.
  • Organisms of interest within the sample are identified by automatically locating viable organisms within the collected sample through the activated stain's fluorescence, e.g., by detecting the glow using fluorescence detection within collected image data, as described in greater detail herein.
  • the sample is treated with a reagent that has a reporter dye that provides a specific spectral response to excitation that can be identified in corresponding image data, as also described in greater detail herein.
  • the sample collected on the filter is treated using an assay, e.g., which includes suitable reagents and corresponding equipment so as to render one or more select types of living organisms detectable for at least a finite amount of time, where the detectable feature is detected within collected image data.
  • the assay may be targeted to at least one particular organism, e.g., that that leaves a marker at least temporarily, that is detected within collected image data, as described more fully herein.
  • the method still further comprises optionally transferring the filter to a microscope at 106 , e.g., where relocation of the treated sample is necessary, and collecting image data of the treated sample using a microscope at 108 .
  • an automatic microscope stage may be used to collect image data by automatically taking at least one focused image of the collected sample.
  • the method also comprises locating at 110 , any potential viable organisms of interest within the collected sample by utilizing image processing to automatically locate whether there are occurrences of the marker within at least one image of the collected sample.
  • the method comprises identifying at 112 , at least one target location within the sample for spectral interrogation of a potential viable organism, each target location selected based upon the location of a corresponding instance of the marker within the sample that is derived from the collected image data.
  • each selected targeted location within the sample corresponds to a region sufficiently small to pinpoint a single potential viable organism within the sample for interrogation.
  • the method still further comprises obtaining spectral information of each identified target location within the sample at 114 and analyzing any obtained spectral information to automatically establish a preliminary identification of at least one corresponding located potential viable organism at 116 , where each preliminary identification is made to at least a small ambiguity of the identification.
  • a Raman spectrometer is utilized to collect a Raman spectrum for each target location, e.g., to measure the spectrum of each located potential viable organism.
  • Image processing is then utilized to automatically locate any potential viable organisms on the filter membrane, e.g., by targeting locations on the membrane identified by a corresponding marker associated with the reagent treatment, such that each marked organism may be analyzed through targeted interrogation by a Raman system to collect spectral data corresponding to each targeted location(s).
  • the method still further comprises confirming the identification at 118 by providing information related to the result of the preliminary identification for a final confirmation by a user.
  • the spectrums of any located viable organisms measured by the Raman spectrometer are automatically analyzed to establish a preliminary identification of any located viable organism(s)/particulate(s), etc., or to identify any viable organism(s)/particulate(s) within a small ambiguity of the identification.
  • the system then provides information related to the viable organisms for final determination, e.g., by providing the collected sample to a visible microscope for user confirmation of the preliminary identification results, by providing an opportunity to the operator to view at least one visual image of the located viable organisms to an operator for verification of the automatically generated preliminary identification, so that the operator may make the final determination as to the identification/classification of the targeted viable organism(s)/particulate(s), etc.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Organic Chemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Biophysics (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Genetics & Genomics (AREA)
  • Biochemistry (AREA)
  • Biotechnology (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Toxicology (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
  • Sampling And Sample Adjustment (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Image Processing (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
US13/260,973 2009-04-03 2010-03-26 Biological and chemical collection and detection Active 2030-03-28 US8547550B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US13/260,973 US8547550B2 (en) 2009-04-03 2010-03-26 Biological and chemical collection and detection

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US16639909P 2009-04-03 2009-04-03
PCT/US2010/028869 WO2010114772A2 (en) 2009-04-03 2010-03-26 Biological and chemical collection and detection
US13/260,973 US8547550B2 (en) 2009-04-03 2010-03-26 Biological and chemical collection and detection

Publications (2)

Publication Number Publication Date
US20120040330A1 US20120040330A1 (en) 2012-02-16
US8547550B2 true US8547550B2 (en) 2013-10-01

Family

ID=42289336

Family Applications (1)

Application Number Title Priority Date Filing Date
US13/260,973 Active 2030-03-28 US8547550B2 (en) 2009-04-03 2010-03-26 Biological and chemical collection and detection

Country Status (4)

Country Link
US (1) US8547550B2 (ja)
EP (1) EP2414536B1 (ja)
JP (1) JP5597248B2 (ja)
WO (1) WO2010114772A2 (ja)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150185076A1 (en) * 2013-12-27 2015-07-02 Nuctech Company Limited Raman spectroscopic detection method
DE102016113748A1 (de) * 2016-07-26 2018-02-01 Leibniz-Institut für Photonische Technologien e. V. Kombiniertes optisch-spektroskopisches Verfahren zur Bestimmung von mikrobiellen Erregern
US11358984B2 (en) 2018-08-27 2022-06-14 Regeneran Pharmaceuticals, Inc. Use of Raman spectroscopy in downstream purification

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101193051B1 (ko) * 2011-08-25 2012-10-22 한국과학기술연구원 항균필터 성능 평가장치 및 방법
WO2013084444A1 (ja) * 2011-12-05 2013-06-13 リオン株式会社 生物粒子計数器、生物粒子計数方法、透析液監視システム及び浄水監視システム
JP6040992B2 (ja) * 2011-12-23 2016-12-07 株式会社ニコン 集積光学アセンブリの改良
JP5968201B2 (ja) * 2012-11-14 2016-08-10 株式会社堀場製作所 着色剤同定方法、及び着色剤同定装置
US9618619B2 (en) 2012-11-21 2017-04-11 Nikon Corporation Radar systems with dual fiber coupled lasers
FR3001544B1 (fr) 2013-01-31 2015-02-27 Commissariat Energie Atomique Procede de reglage de la position relative d'un analyte par rapport a un faisceau lumineux
FR3002635B1 (fr) * 2013-02-27 2015-04-10 Areva Nc Systeme pour l'analyse, par spectrometrie de plasma induit par laser, de la composition de la couche superficielle d'un materiau et pour le prelevement d'echantillons en vue d'analyses complementaires ou de controles de cette couche superficielle, et procede y relatif
US9664658B2 (en) 2015-01-13 2017-05-30 Src, Inc. Method, device, and system for aerosol detection of chemical and biological threats
JP6534318B2 (ja) * 2015-09-02 2019-06-26 アズビル株式会社 蛍光粒子の計測方法
EP3853616A4 (en) * 2018-09-20 2021-11-17 Siemens Healthcare Diagnostics, Inc. HYPOTHESIS AND VERIFICATION NETWORKS AND PROCEDURES FOR SAMPLE CLASSIFICATION
SG10201913005YA (en) * 2019-12-23 2020-09-29 Sensetime Int Pte Ltd Method, apparatus, and system for recognizing target object

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5518883A (en) 1992-07-02 1996-05-21 Soini; Erkki J. Biospecific multiparameter assay method
US5701012A (en) * 1996-03-19 1997-12-23 Her Majesty The Queen In Right Of Canada, As Represented By The Minister Of National Defence Fluorescent biological particle detection system
US5891738A (en) 1995-01-16 1999-04-06 Erkki Soini Biospecific multiparameter assay method
EP1624071A2 (en) 2004-08-06 2006-02-08 Fuji Electric Holdings Co., Ltd. Method of detecting viable cells
US20060269923A1 (en) 2003-03-27 2006-11-30 Trotta Christopher R Methods of identifying compounds that target trna splicing endonuclease and uses of said compounds as anti-fungal agents
WO2007021485A2 (en) 2005-08-17 2007-02-22 Chemimage Corporation Raman spectral analysis of pathogens
US20070087430A1 (en) * 2005-10-17 2007-04-19 Sword Diagnostics, Inc. Method and apparatus for detection of biological organisms using raman scattering
US20080274489A1 (en) * 2005-10-17 2008-11-06 Sword Diagnostics, Inc. Methods for detecting organisms and enzymatic reactions using raman spectroscopy

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000068434A2 (en) * 1999-05-07 2000-11-16 Yale University Multiple tag analysis
AU2003212794A1 (en) * 2002-01-10 2003-07-30 Chemlmage Corporation Method for detection of pathogenic microorganisms
US20050147976A1 (en) * 2003-12-29 2005-07-07 Xing Su Methods for determining nucleotide sequence information

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5518883A (en) 1992-07-02 1996-05-21 Soini; Erkki J. Biospecific multiparameter assay method
US5891738A (en) 1995-01-16 1999-04-06 Erkki Soini Biospecific multiparameter assay method
US5701012A (en) * 1996-03-19 1997-12-23 Her Majesty The Queen In Right Of Canada, As Represented By The Minister Of National Defence Fluorescent biological particle detection system
US7428045B2 (en) * 2002-01-10 2008-09-23 Chemimage Corporation Raman spectral analysis of pathogens
US20060269923A1 (en) 2003-03-27 2006-11-30 Trotta Christopher R Methods of identifying compounds that target trna splicing endonuclease and uses of said compounds as anti-fungal agents
EP1624071A2 (en) 2004-08-06 2006-02-08 Fuji Electric Holdings Co., Ltd. Method of detecting viable cells
WO2007021485A2 (en) 2005-08-17 2007-02-22 Chemimage Corporation Raman spectral analysis of pathogens
US20070087430A1 (en) * 2005-10-17 2007-04-19 Sword Diagnostics, Inc. Method and apparatus for detection of biological organisms using raman scattering
US20080274489A1 (en) * 2005-10-17 2008-11-06 Sword Diagnostics, Inc. Methods for detecting organisms and enzymatic reactions using raman spectroscopy

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
J. Noh et al., "Combined micro-Raman/UV-visible/fluorescence spectrometer for high-throughput analysis of microsamples." Review of Scientific Instruments, vol. 78, 2007, pp. 72205-1 to 72205-6, XP002599773, College Park, MD USA.
Japanese Office Action dated Jul. 2, 2013 for Japanese Patent Application No. 2012-503533, filed Sep. 28, 2011; on page one of the Japanese Office action.
Notification of Transmittal of the International Preliminary Report on Patentability for PCT Application No. PCT/US2010/028869, mailing date of Oct. 18, 2011; International Preliminary Report on Patentability, European Patent Office, Rijswijk, Netherlands.
Notification of Transmittal of the International Search Report and the Written Opinion of the International Searching Authority for PCT Application No. PCT/US2010/028869; Mailing Date of Sep. 28, 2010; European Patent Office, Rijswijk, The Netherlands.
Rosch, Petra et al., "Fast and Reliable Identification of Microorganisms by Means of Raman Spectroscopy", Proc. SPIE, 2007, vol. 6633, p. 66331A.1-66331A.9.
Written Opinion of the International Preliminary Examining Authority for PCT Application No. PCT/US2010/028869; Mailing Date of Aug. 2, 2011; European Patent Office, Rijswijk, The Netherlands.

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150185076A1 (en) * 2013-12-27 2015-07-02 Nuctech Company Limited Raman spectroscopic detection method
US10267678B2 (en) * 2013-12-27 2019-04-23 Nuctech Company Limited Raman spectroscopic detection method
DE102016113748A1 (de) * 2016-07-26 2018-02-01 Leibniz-Institut für Photonische Technologien e. V. Kombiniertes optisch-spektroskopisches Verfahren zur Bestimmung von mikrobiellen Erregern
US10969332B2 (en) 2016-07-26 2021-04-06 Leibniz-Institut Für Photonische Technologien E.V. Combined optical-spectroscopic method for identifying microbial pathogens
US11358984B2 (en) 2018-08-27 2022-06-14 Regeneran Pharmaceuticals, Inc. Use of Raman spectroscopy in downstream purification

Also Published As

Publication number Publication date
JP2012521781A (ja) 2012-09-20
US20120040330A1 (en) 2012-02-16
WO2010114772A3 (en) 2010-11-25
EP2414536A2 (en) 2012-02-08
WO2010114772A2 (en) 2010-10-07
JP5597248B2 (ja) 2014-10-01
EP2414536B1 (en) 2014-06-18

Similar Documents

Publication Publication Date Title
US8547550B2 (en) Biological and chemical collection and detection
Morris Modern microscopic methods of bioaerosol analysis
US8852892B2 (en) Physical geolocation system
US11280720B2 (en) Cell analysis method, cell analyzer and sample screening method
US20080102487A1 (en) Method and apparatus for non-invasive rapid fungal specie (mold) identification having hyperspectral imagery
JP2013246140A (ja) 情報処理装置、情報処理方法、及びプログラム
Rittenour et al. Immunologic, spectrophotometric and nucleic acid based methods for the detection and quantification of airborne pollen
JP2017058361A (ja) 情報処理装置、情報処理方法及び情報処理システム
Ezegbogu Identifying the scene of a crime through pollen analysis
Negron et al. Using flow cytometry and light-induced fluorescence to characterize the variability and characteristics of bioaerosols in springtime in Metro Atlanta, Georgia
JP4487985B2 (ja) 微生物計量装置
JP6987837B2 (ja) 抗菌剤感受性予測のためのフローサイトメトリデータ処理
JP6987838B2 (ja) 抗菌剤感受性予測のためのフローサイトメトリデータ処理
JP5799086B2 (ja) 分類学的階層分類を用いる微生物因子の同定及び／又はキャラクタリゼーション
Mermans et al. Opportunities in optical and electrical single-cell technologies to study microbial ecosystems
Gopalakrishnan et al. Comparison and evaluation of enumeration methods for measurement of fungal spore emission
Jang et al. Application of Cytosense flow cytometer for the analysis of airborne bacteria collected by a high volume impingement sampler
Sandle Real-time counting of airborne particles and microorganisms: A new technological wave?
CA2536698A1 (en) System and method of detecting, identifying and characterizing pathogensand characterizing hosts
CN115639354B (zh) 一种海洋塑料识别方法及装置
Gottardini et al. Automated microscopy techniques on passively collected samples provide reliable quantitative data on airborne pollen
US20090205977A1 (en) Method and system for detecting a target with a specific marker
US20230041482A1 (en) System and method for detecting microbial agents
Schaldach et al. Non-DNA methods for biological signatures
Silge et al. A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Legal Events

Date Code Title Description
STCF Information on status: patent grant

Free format text: PATENTED CASE

FPAY Fee payment

Year of fee payment: 4

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Year of fee payment: 8